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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. One day we're gonna

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Speaker 1: touch on the thought situation.

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Speaker 2: Jesus, but no, I'm too frou.

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Speaker 1: We can have a possum update.

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Speaker 2: Yeah, tell us about the possum. You cleaned it up.

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Speaker 1: I cleaned it up. That's that's really the update. I

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Speaker 1: don't want to say that this show is my therapy,

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Speaker 1: because it is truly not that this show.

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Speaker 2: Is what you talk about in therapy.

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Speaker 1: But it isn't the case that, Like, as I was

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Speaker 1: talking about the possum last week, I knew that editors

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Speaker 1: were not going to cut it out and that I

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Speaker 1: was using it as a commitment device to finally make

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Speaker 1: myself just go out there and shovel up the dead

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Speaker 1: possum in my heart. Im. That's if you didn't listen

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Speaker 1: to the last week's episodes.

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Speaker 2: Yeah, wow, this would be horrifying. Go back listen to

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Speaker 2: that or don't interest it's interesting.

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Speaker 1: This is just like this is the like skip ahead banter, Awa. Yeah,

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Speaker 1: I cleaned up the possum. And then then my wife

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Speaker 1: was like, I listen to your podcast, I learned about

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Speaker 1: the possum.

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Speaker 2: That's a good test to find out if your spouse

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Speaker 2: listens and tests. I don't. I don't think Joe listens

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Speaker 2: because he didn't call me out on the fact that

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Speaker 2: I just wait for him to notice the vomit from the.

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Speaker 1: See he knew that. Yeah, this is like densely packed

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Speaker 1: with references.

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Speaker 2: Yeah, a lot of callbacks.

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Speaker 1: Yeah.

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Speaker 2: He probably also knows that because sometimes I'll say, Hey,

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Speaker 2: the cat vomited again, could you co get that right?

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Speaker 1: And other times you'll be sitting pointedly facing away from

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Speaker 1: the cat vomit and he'll be like, I know that,

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Speaker 1: you know there's cat vomit there, and you're like, what, we.

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Speaker 2: Have a cat psychological warfare.

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Speaker 1: Hello, Welcome to the Money Stuff Podcast. You're Herley podcast

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Speaker 1: where we talk about stuff. I led it to Money.

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Speaker 1: I'm Matt Levine and I write The Money Stuff Colum

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Speaker 1: from Bloomberg Opinion.

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Speaker 2: And I'm Katie Greyfell, a reporter for Bloomberg News and

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Speaker 2: an anchor for Bloomberg Television.

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Speaker 1: Polly Market. Yeah, so Bloomberg News. How a story this

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Speaker 1: week that Shane Coplin, the founder of Polymarket, is now

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Speaker 1: the world's youngest self made billionaire, which.

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Speaker 2: Is cool for him, definitely.

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Speaker 1: Shane Coplin lives or used to live next door to

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Speaker 1: a guy who throws a lot of book parties for

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Speaker 1: finance books. Oh, so you can see him like all

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Speaker 1: the finance book parties.

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Speaker 2: That's your kind of hangout.

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Speaker 1: Sadly, yes, and so yeah, I've seen Shane Coplin at book.

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Speaker 2: Parties and you've rubbed elvas.

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Speaker 1: I've rubbed Elvas. And when I saw that article, I

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Speaker 1: thought youngest self made billionaire. I realized that Shane Coplin

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Speaker 1: is the second world's youngest self made billionaire to be

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Speaker 1: in my phone context. It's not like an entirely positive development.

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Speaker 2: For right now. It is a positive development for Shane.

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Speaker 2: Will continue to watch this space.

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Speaker 1: It is also he did at the right order though

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Speaker 1: first he was investigative by the government, and now he's

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Speaker 1: a self made billionaire.

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Speaker 2: I was going to say, I forgot that it was

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Speaker 2: less than a year ago that his apartment was rated

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Speaker 2: by the Yeah, fast forward to October twenty twenty five,

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Speaker 2: he's super rich. ICE. Of course, the news from this

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Speaker 2: week was that they invested.

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Speaker 1: The inter Intercontinental Exchange.

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Speaker 2: We're not we're using government names.

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Speaker 1: ICE can mean a number of things. Anyway, gone anyway.

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Speaker 2: Ice, the Intercontinental Exchange investing two billion dollars in polymarket.

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Speaker 2: That gives it an eight billion dollar valuation. Pretty stunning.

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Speaker 2: And it was just last week that we were talking

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Speaker 2: about how prediction markets are eating everything and here we

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Speaker 2: are keep.

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Speaker 1: Going yeah right, it's funny, like I think of Calshi

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Speaker 1: as a sports gambling site, piymarket, I don't know, probably

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Speaker 1: market like feels a little pure. Yeah, you know this

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Speaker 1: the bloomerg story about Shane Complin's like founding a polymarket.

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Speaker 1: You know, he's like reading economics papers about prediction markets.

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Speaker 1: I'm thinking this is too good an idea to just

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Speaker 1: exist on white papers, and like I think he's like

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Speaker 1: fully committed to the notion of Yeah, but like you know,

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Speaker 1: they did get into sportscambling. Yeah, and I think that

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Speaker 1: I'm not sure, but my sense is that it is

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Speaker 1: hard to justify an eight billion dollar valuation for a

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Speaker 1: prediction market that is just elections and uh, you know,

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Speaker 1: who will be a time man of the person of

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Speaker 1: the year and uh and uh, you know, like just

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Speaker 1: fun predictions and not you know, for addicted sports candlers.

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Speaker 2: I will say that, you know, if you had told

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Speaker 2: me that Ice had outright bought poly Market for two

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Speaker 2: billion dollars, I still would have been like, Wow, that's

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Speaker 2: a lot of money. The fact that it does have

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Speaker 2: this eight billion dollar valuation also stuck out to.

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Speaker 1: Me, Right, I just think that that that valuation discounts

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Speaker 1: a lot of growth in sports, yeah, and a lot

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Speaker 1: of margins in sports and not the other thing. But

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Speaker 1: I'm intrigued, right when you read the announcement, like and

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Speaker 1: again when you read like Shane Coplan's public statements like like, yeah,

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Speaker 1: there's sports, but like they're not they're not leaning on

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Speaker 1: the sports. It's not like Robin Hood saying sportscambling is

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Speaker 1: an emerging hasset class. Yeah, they're like, eh, we're like

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Speaker 1: trying to find ways to understand and price the future.

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Speaker 1: And I think that's like a noble go like like

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Speaker 1: prediction markets, Like like he's not wrong that, like economists

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Speaker 1: have talked about them for years, Like the idea of

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Speaker 1: prediction markets, like providing a set of probabilities about the future,

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Speaker 1: does seem really socially useful. And my sense was always

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Speaker 1: that no one had cracked the nut, not of like

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Speaker 1: creating trading infrastructure or making it legal, but then not

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Speaker 1: of like making people want to trade it, such that

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Speaker 1: there was like a big market, a big liquid market

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Speaker 1: for it, such that professionals would have incentives to make

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Speaker 1: prediction market prices correct, right, yeah, because like and once like, oh,

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Speaker 1: let's gamble on whether like you know, the Ukraine War

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Speaker 1: will end, like I mean, people are but like, it's

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Speaker 1: not like a fun gambling product and so there's not

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Speaker 1: a lot of money to be made if you have

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Speaker 1: good insight into it. And so just the whole thing

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Speaker 1: has never quite worked right, and like y US presidential

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Speaker 1: elections where there's a lot of money, but otherwise it

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Speaker 1: never never feels like it quite works in sports or

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Speaker 1: you know, sports solve that problem. Yeah, people really want

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Speaker 1: to gamble on sports, and there's a lot of dumb

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Speaker 1: money and so there's a lot of value for smart

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Speaker 1: money to bet against, and so you have incentives to

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Speaker 1: build good sports prediction models and trade on polymarket and

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Speaker 1: all kind of works. And then you know, the question

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Speaker 1: is that does that spill over into the New York

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Speaker 1: City mayor race or you know macroeconomic variables or like

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Speaker 1: you know, the Grammys or whatever.

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Speaker 2: Yeah, well I do like that. You brought up again

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Speaker 2: the Grossman s tigletz paragdox in your call them on

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Speaker 2: this specific item. You're calling back to another column that

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Speaker 2: you know, maybe the way that we get to that

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Speaker 2: societal useful information is through the avenue of sports betting

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Speaker 2: and a lot of people making a lot of noise, right,

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Speaker 2: Like the gross and.

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Speaker 1: Said, what's paradox is the idea that like, you can't

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Speaker 1: have efficient markets because then no one would have incentives

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Speaker 1: to trade and make the markets efficient. Yeah, so Lassie

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Speaker 1: Peterson says that you need efficiently inefficient markets. You need

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Speaker 1: like just the right level of inefficiency to incentivize people

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Speaker 1: to make them more efficient. And like the economisty of

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Speaker 1: prediction markets is like people who have particular insight into

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Speaker 1: like whether there'll be a war will like trade their insights,

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Speaker 1: but like who will trade against them? Like who's just

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Speaker 1: like randomly wandering around like, oh, I bet there won't

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Speaker 1: be a war, and then like you know, get suckered

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Speaker 1: by the policy experts and in the stock market, Like

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Speaker 1: the answer to those questions they're super easy, right, The

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Speaker 1: answer to like who will trade against the hedge fund

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Speaker 1: is an index fund who is just blindly managing retirement

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Speaker 1: money and needs to invest it in stocks, And so

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Speaker 1: the hedge fund has like someone they know those trade against,

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Speaker 1: or the answer is like you know a retail investor or, Like,

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Speaker 1: there's a lot of straightforward answers to like who is

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Speaker 1: on the other side of this trade that allow people

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Speaker 1: with a lot of information to make money by trading

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Speaker 1: and incorporating that information enter prices in prediction markets. It

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Speaker 1: was never clear who those people were, right, Like, it's

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Speaker 1: not a savings product, right, Like people don't put aside

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Speaker 1: one hundred dollars a month in retirement savings in prediction markets,

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Speaker 1: so there's no like uninformed flow to trade against there.

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Speaker 1: And it has historically not been a super fun gambling market,

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Speaker 1: like with the exception of presidential elections and a few

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Speaker 1: other high profile things. But when you add sports, it

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Speaker 1: becomes a fun gambling market, and then that just opens

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Speaker 1: everything up, right, It allows people to make money making

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Speaker 1: informed predictions at least on sports and then maybe at

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Speaker 1: most of something else.

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Speaker 2: Yeah, I mean just assume that the people who are

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Speaker 2: betting on sports on the prediction market go into other

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Speaker 2: markets such as I don't know, elections or weather or

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Speaker 2: whatever else.

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Speaker 1: Well, it's both, right, So it's like, on the one hand,

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Speaker 1: like do you attract dumb money recreational gamblers to the platform,

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Speaker 1: and then they're like, well, i'm here anyway, I might

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Speaker 1: as well bet on election. Its like that seems plausible.

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Speaker 1: And the other thing is like, you know, if you

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Speaker 1: are a quantitative trading firm and you have historically traded

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Speaker 1: like stocks and options, a lot of those people are

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Speaker 1: now getting sports curious, right, Like they're building sports trading desks.

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Speaker 1: They're like building models to price sports events. Because it's

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Speaker 1: like kind of the same skill set, right, It's like

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Speaker 1: taking machine learning and applying it to a bunch of

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Speaker 1: data and like using it to predict the future. And

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Speaker 1: it's like, well, you can do that with stocks, you

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Speaker 1: can do it with sports. And there's a lot of

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Speaker 1: money to made. And so some quant trading firms are

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Speaker 1: either heading into the sports market making business or the

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Speaker 1: traders are leaving to start sports market making businesses. And

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Speaker 1: if those people who are trading on traditional sports books

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Speaker 1: or who are like market making on calci, if those

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Speaker 1: people start market making on polymarket in sports, it's like

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Speaker 1: a relatively easy lift for them to add an election column, right,

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Speaker 1: Like it's a whole new set of data analysis, but

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Speaker 1: they're already plugged in, you know, they're already like now,

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Speaker 1: the market structure works. So maybe if you're making money

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Speaker 1: trading against recreational gamblers on sports and you see election

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Speaker 1: prices that you think are out of line, you're like, well,

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Speaker 1: I put a few million dollars on that election too.

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Speaker 2: While I'm here, I might as well, Well it's just.

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Speaker 1: Right, it's convenient there.

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Speaker 2: Yeah, So what does ICE get out of this? I

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Speaker 2: don't know, That's the thing.

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Speaker 1: I mean, one answer is an investment, right, I mean

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Speaker 1: there's two things.

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Speaker 2: Right.

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Speaker 1: It's like, if it's the future of sports gambling, that's valuable.

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Speaker 1: And then like if like the way you trade your

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Speaker 1: insights into like whether there will be inflation changes from

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Speaker 1: like trading treasuries to like buying the inflation contract on polymarket,

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Speaker 1: Like that's a big deal, right, And I don't know

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Speaker 1: that that is a near term likely outcome. One, it's

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Speaker 1: a possibility, and two becomes more likely when you partner

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Speaker 1: with a big exchange firm, you know, like ICE now

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Speaker 1: when they're thinking about like what kind of like macroeconomic

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Speaker 1: products should we offer. Now, in addition to like interest

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Speaker 1: rate futures and bond futures, they can offer predictions of

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Speaker 1: inflation right, So that's one thing they get is like

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Speaker 1: the that's like currently in the cards, right, Like the

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Speaker 1: current situation is like they're going to distribute polymarket data,

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Speaker 1: which is of interest to people who trade on and

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Speaker 1: the other think it's tokenization, which who knows. That can

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Speaker 1: mean a lot of things. Yeah, it could be like

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Speaker 1: technological infrastructure for like trading ice products, where like instead

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Speaker 1: of buying great futures, you buy great futures tokens, right,

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Speaker 1: But it could also mean, like everyone talks a big

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Speaker 1: game about stock tokenization. Everyone, it's a big game about

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Speaker 1: tokenizing private companies.

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Speaker 2: Right.

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Speaker 1: There's a lot of stuff in that space, and like

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Speaker 1: it is possible that Polymarket, which is crypton native and

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Speaker 1: kind of fun, is better positioned to do some of

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Speaker 1: those initiatives than like the New York Stock Exchange, right,

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Speaker 1: which is the New York Stock Exchange, right, and which

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Speaker 1: has like if you're the New York Stock Exchange, like

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Speaker 1: we're gonna make everything tokenized. Like everyone who's already trading

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Speaker 1: on the stock exchange is gonna be very annoyed. Yeah,

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Speaker 1: Like Polymarket you can tokenize whatever they want.

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Speaker 2: Tokenization has been on my list of things to actually

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Speaker 2: think about for a while. See, but I haven't gotten there, Matt.

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Speaker 1: I've written about it. It's funny. It's like, to me,

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Speaker 1: tokenization means two things. Like one, there's like some amount

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Speaker 1: of trading in tradition financial markets. You could change the

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Speaker 1: back end of the market structure and make it so

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Speaker 1: that instead of being on some company's ledgers, it's on

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Speaker 1: a blockchain and you can call it tokenized.

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Speaker 2: And it's all a little people that.

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Speaker 1: Yeah, it's the sort of thing that like ten years ago,

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Speaker 1: you'd be like, it'll be a different database for your

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Speaker 1: interest rate futures. You'd be like, I don't care about that.

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Speaker 1: But now it's like, oh, it's tokenized, right, So that's

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Speaker 1: like like one thing is like the actual technological stuff,

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Speaker 1: and like it's stuff about like how things work together

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Speaker 1: and how like if you're an hedgephund you can like

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Speaker 1: move your tokens from one platform to another, and like

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Speaker 1: you can you know, live in the same blockchain environment

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Speaker 1: for different kinds of trades, right, Like you can trade

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Speaker 1: crypto against futures. I don't know, there's like that technological

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Speaker 1: market structure stuff. And then like to me, it always

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Speaker 1: seems like when people talk about tokenization what they're always

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Speaker 1: talking about is getting around securities loss, and that's not

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Speaker 1: always always true. I don't know that that's what Ice

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Speaker 1: and polymarket are talking about. It's what Robin is talking

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Speaker 1: about every time they talk about togonization, right, And it's like,

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Speaker 1: you see, like people like we should tokenize private companies

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Speaker 1: so that you can buy shares of private companies without

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Speaker 1: having them have to go public. It's like, well, that's

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Speaker 1: not how the securities laws works. But if we call

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Speaker 1: a share a token, then they don't have to follow

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Speaker 1: the law.

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Speaker 2: Yeah, so he struck me as.

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Speaker 1: Wrong, but it might turn out to be empirically correct.

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Speaker 1: But anyway, so there's two kinds of organization. I've etten

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Speaker 1: a lot about the bad regulatory arbitrage kind, but like

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Speaker 1: it's possible, the technological kind is important, meaningful, we'll do something.

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Speaker 2: Well, hopefully these initiatives will bear fruit and we can

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Speaker 2: actually figure out what Ice and polymarket are talking about.

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Speaker 1: Yeah, I will say that, I like, I have spent

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Speaker 1: ten years writing about financial infrastructure firms saying we're doing

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Speaker 1: a blockchain initiative.

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Speaker 2: It's all about the tech, and then like five years later.

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Speaker 1: They're like, we have stopped their blockchain initiative.

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Speaker 2: Stop asking about it. We don't want to talk about.

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Speaker 1: It, right, So it's puzzle tokenization. Is that it's puzzle tokenization.

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Speaker 1: It's like we're putting the futures on the blockchain and

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Speaker 1: then five years later we'll stop talking about it. Yes,

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Speaker 1: I've thought a lot about taking anosition, but I don't

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Speaker 1: have any I don't know what I mean. It's either.

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Speaker 2: I feel like I talked about raboris on this show before. Yeah, yeah,

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Speaker 2: the snake eating itself. Well, another way of saying that is,

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Speaker 2: you know, all these big tech companies are just spending

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Speaker 2: money in a circle, and this week I feel like

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Speaker 2: that idea was really on display. When you think about

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Speaker 2: open ai and a m D and the deal that

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Speaker 2: they inked, it's great. Yeah. Yeah, So my understanding basically

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Speaker 2: is that specifically with a m D, so they agreed

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Speaker 2: to deploy a bunch of a m D chips and

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Speaker 2: as part of that, they're also getting a bunch of

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Speaker 2: warrants for AMD shares as well.

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Speaker 1: Yeah, there's kind of two wild ass ways of it.

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Speaker 1: I mean there's at least seventeen, but like two wild

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Speaker 1: asspays of it. One is that open Ai has committed

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Speaker 1: to spend I don't know the number, but it's like

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Speaker 1: it's six gigawatts of chips. Yeah, and they've said it's

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Speaker 1: like tens of billions of dollars per So the open

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Speaker 1: eye is committed to spend something on the order of

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Speaker 1: one hundred billion dollars buying chips from AIM and open

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Speaker 1: A doesn't have one hundred billion dollars. No. One thing

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Speaker 1: that is happening here is that the market has ascribed

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Speaker 1: a value to open ai of like, you know, half

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Speaker 1: a trillion dollars, and that value is sufficiently real that

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Speaker 1: open and ai can like make financial commitments based on it.

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Speaker 1: They can be like, yeah, we'll give you a hundred

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Speaker 1: million dollars for your chips, and they're great, and it's

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Speaker 1: like everyone's like, yeah, that'll work out right, But like

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Speaker 1: there's no, it's not in the bank, Like that's like, yeah,

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Speaker 1: it's five million dollars company, they'll find the money. And

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Speaker 1: I don't think that's a wrong bet. It's just an

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Speaker 1: interesting they're able to commit cash based not on the

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Speaker 1: cash they have, but on their valuation. And then the

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Speaker 1: other thing that's happening is that like when open ai

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Speaker 1: says anything about anyone, the stock goes up. And so

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Speaker 1: when Opening I announces a deal of like we're gonna

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Speaker 1: send one hundred million dollars on AMTI chips, AMD stock

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Speaker 1: very predictably goes up. And so knowing that as they

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Speaker 1: negotiated the deal, they're like, what we should do is

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Speaker 1: we should take your very predictable stock price rise and

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Speaker 1: use that to pay for the chips. Right like, essentially,

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Speaker 1: like AMD shareholders will get excited about AMD having this deal,

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Speaker 1: and so we'll let the AMD shareholders pay for the chips.

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Speaker 1: And so that's kind of what happened, which is that

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Speaker 1: AMD stock we're recruiting this on Wednesday at a stock

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Speaker 1: is up roughly one hundred billion dollars from where it

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Speaker 1: was last Friday before they announced the deal, and you

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Speaker 1: know that'll covers the chips. And open Ai, which as

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Speaker 1: far as we know, plans to pay cash for these chips. Yeah, yeah,

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Speaker 1: I don't think the deal is like we won't pay

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Speaker 1: for the chips. The deals. We will pay for the chips.

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Speaker 1: But Open Eye is getting warrants for roughly ten percent

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Speaker 1: of AMD with like vesting conditions, but they're like penny

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Speaker 1: warrants and so right Now, if you just assume they'll

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Speaker 1: get all of the warrants, that's like a thirty seven

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Speaker 1: billion dollar ish yeah, value transferred to open Ai. Yeah,

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Speaker 1: which one will help pay for the cost of this contract.

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Speaker 1: And two it is only fair because like they created

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Speaker 1: all that extra value at AMD, right, Like they're the

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Speaker 1: ones who by sprinkling their magic dust on AMD, we're

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Speaker 1: able to make AMD more valuable. And so yeah, they

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Speaker 1: get back half that value in warrants.

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Speaker 2: Yeah. Well two things. So to your point that you

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Speaker 2: know open ai mentions some sort of whatever with another company,

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Speaker 2: a public company, their stare price goes up.

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Speaker 1: Yeah.

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Speaker 2: It makes me think about what we keep talking about

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Speaker 2: in terms of the private and public markets converging. Obviously,

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Speaker 2: open Ai famously is not public, but has been making

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Speaker 2: a ton of waves in the public stock market. The

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Speaker 2: other thing is, you know what we saw with open

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Speaker 2: Ai and AMD this week isn't a one off. They

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Speaker 2: have something similar with in Video, which of course is

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Speaker 2: a direct rival of AMD.

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Speaker 1: Yeah, it's like very different, but it's like from at

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Speaker 1: a high level, the same thing, Right, it's like we

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Speaker 1: have a partnership where I think in that case, in

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Speaker 1: Video is investing in open Ai and then Yeah.

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Speaker 2: In video agreed to invest as much as one hundred

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Speaker 2: billion dollars in open Ai to help open ai fund

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Speaker 2: a data center build out in Exchange. Open Ai committed

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Speaker 2: to filling those data centers with millions of in Nvidia chips.

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Speaker 2: This is a little bit old, but I was reading

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Speaker 2: this piece from Michael sembelest Over at JP Morgan and

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Speaker 2: he wrote this at the end of September. But it

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Speaker 2: spiritually can apply to all of these deals that Oracle

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Speaker 2: stock jump by twenty five percent after being promised sixty

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Speaker 2: billion dollars a year from open Ai in amount of

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Speaker 2: money open Ai doesn't earn yet to provide cloud computing

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Speaker 2: facilities that Oracle hasn't built yet and which will require

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Speaker 2: four point five gigwats of power, which is the equivalent

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Speaker 2: of several Hoover dams. So it's easy, if you wanted

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Speaker 2: to get scared and flustered, to build a case that

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Speaker 2: there's a bubble being inflated right now.

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Speaker 1: I don't disagree. It's unusually easy to like visualize the future,

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Speaker 1: right It's usually easily to be like Ai is going

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Speaker 1: to be huge, m It's going to transform every aspect

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Speaker 1: of life. It's going to require a lot of power,

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Speaker 1: and like that requirement is going to be so obvious

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Speaker 1: that like we'll build the power plants. It's going to

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Speaker 1: require a lot of data centers, and that requirement is

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Speaker 1: going to be so obvious. We'll build the data centers

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Speaker 1: and it's going to rake in oceans of money. Yeah,

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Speaker 1: and that is so obvious that like the people who'll

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Speaker 1: be raking in the money open AI I can just

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Speaker 1: spend that money now, you know, Yeah, sixty billion dollars year,

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Speaker 1: No problem, we'll get that, we'll up. Yeah, Like this

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Speaker 1: is what capital markets are supposed to do, which is

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Speaker 1: like discount the future. And here there's like unusually widespread

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Speaker 1: consensus on like how big and transformative this future will

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Speaker 1: be and like what the steps are to get there,

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Speaker 1: and so it's all being discounted right now into you know,

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Speaker 1: the price of Open Eye. But also like these enormous,

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Speaker 1: long term, incredibly capital intensive deals where everyone's like, yeah,

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Speaker 1: of course that's going to get fine.

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Speaker 2: It's like that's no problem, don't worry about it.

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Speaker 1: And like one like that is the consensus view, right,

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Speaker 1: Like that is what people think.

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Speaker 2: Right, very strong, it's very much so it's.

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Speaker 1: Not like a priori crazy to be like, yes, we

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Speaker 1: can see how this is going to go, and so

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Speaker 1: we're putting our money into it now. On the other hand, too,

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Speaker 1: like yeah, if you want to take the contrarian view,

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Speaker 1: like that sounds like a bubble. Yeah, that does sound

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Speaker 1: like of course it sounds like a bubble.

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Speaker 2: Yeah, because there is a risk that we are a

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Speaker 2: risk we're overbuilding here. For sure, A is going to

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Speaker 2: change the future. I feel like it's hard to argue

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Speaker 2: with that. But do we need this money? Gigawaws? Do

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Speaker 2: we need this money?

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Speaker 1: I don't know how many giggs we need? Come on

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Speaker 1: you and like, you know, what he says kind of goes,

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Speaker 1: but like, I don't think it would be crazy to

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Speaker 1: think that, like the current consensus is wrunging in either

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Speaker 1: direction by a factor of two, right, and if it's

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Speaker 1: like low by a factor too, then like everyone's getting

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Speaker 1: get rich, right, And if it's high, then like all

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Speaker 1: these deals will look a little bubbly. But like still,

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Speaker 1: you know, and you know, in like ten years, like hey,

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Speaker 1: I will have been transformative, and I'm we're glad we

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Speaker 1: have many of those data centers.

433
00:22:26,680 --> 00:22:30,119
Speaker 2: Yeah, for sure. Internet comparisons et cetera. You can make

434
00:22:30,160 --> 00:22:32,600
Speaker 2: them here. But instead I'm going to say that there

435
00:22:32,840 --> 00:22:35,320
Speaker 2: was an interesting crack that seemed to form this week.

436
00:22:35,400 --> 00:22:38,480
Speaker 2: Did you see that report by the Information on Oracle. Yes,

437
00:22:38,880 --> 00:22:42,159
Speaker 2: basically it's cloud margins are super thin. To give you

438
00:22:42,240 --> 00:22:46,399
Speaker 2: the details. According to the Information, so Oracle's cloud business

439
00:22:46,480 --> 00:22:51,080
Speaker 2: has narrow margins that many analysts anticipated. They generated about

440
00:22:51,200 --> 00:22:55,560
Speaker 2: nine hundred million dollars in sales by renting servers powered

441
00:22:55,600 --> 00:22:58,600
Speaker 2: by in video chips, but it's gross profit came in

442
00:22:58,760 --> 00:23:01,760
Speaker 2: at fourteen cents for every one dollars in sale. Again,

443
00:23:02,080 --> 00:23:07,120
Speaker 2: this is according to the Information, which is like retailer margins,

444
00:23:07,160 --> 00:23:08,360
Speaker 2: which is pretty crazy.

445
00:23:09,080 --> 00:23:10,920
Speaker 1: Not that I mean, it's a huge business, right, and

446
00:23:11,000 --> 00:23:13,320
Speaker 1: so like, one question is like, who are going to

447
00:23:13,359 --> 00:23:16,080
Speaker 1: be the like monopoly players in this business? And who

448
00:23:16,160 --> 00:23:20,320
Speaker 1: are going to be the like in the fiercely competitive

449
00:23:21,359 --> 00:23:24,359
Speaker 1: business of selling to those monopolists, right? And like, right,

450
00:23:24,440 --> 00:23:27,320
Speaker 1: it's very possible that Oracle is in a commodity business.

451
00:23:27,920 --> 00:23:31,920
Speaker 1: And my sense is that people think that in Vidia

452
00:23:32,119 --> 00:23:34,480
Speaker 1: is very much not in a commodity business. Right, That

453
00:23:34,520 --> 00:23:36,680
Speaker 1: could be wrong, right, And my sense that people think

454
00:23:36,720 --> 00:23:38,560
Speaker 1: open AA is not in a commodity business. That open

455
00:23:38,600 --> 00:23:43,120
Speaker 1: AA has something special and so when it name checks

456
00:23:43,119 --> 00:23:45,520
Speaker 1: a public company, that company goes up. Right, And like

457
00:23:45,640 --> 00:23:47,240
Speaker 1: in Vidia has the same power, right, like the in

458
00:23:47,359 --> 00:23:49,480
Speaker 1: video will sign a partnership with someone and their stock

459
00:23:49,520 --> 00:23:52,119
Speaker 1: will go up. But like it's not clear that, like

460
00:23:52,240 --> 00:23:55,280
Speaker 1: the people who run the data centers have the same

461
00:23:55,320 --> 00:23:55,920
Speaker 1: pricing power.

462
00:23:56,640 --> 00:23:58,600
Speaker 2: Just wonder how the snake dies in the end, you know,

463
00:23:59,440 --> 00:24:02,440
Speaker 2: is it because it chokes or because it's being eaten?

464
00:24:04,359 --> 00:24:04,520
Speaker 1: You know?

465
00:24:04,800 --> 00:24:07,080
Speaker 2: Do you ever think about that constantly?

466
00:24:23,359 --> 00:24:25,119
Speaker 1: I want to talk about too, miscellan things I do

467
00:24:25,240 --> 00:24:27,880
Speaker 1: want to talk about. You had Kathy Wood on your show,

468
00:24:28,040 --> 00:24:30,200
Speaker 1: Yeah I did, and you talked about.

469
00:24:30,000 --> 00:24:34,560
Speaker 2: The other show. Yeah, swimming is not your show.

470
00:24:34,760 --> 00:24:35,520
Speaker 1: This is your show.

471
00:24:35,760 --> 00:24:37,320
Speaker 2: The other show the E t f I S E

472
00:24:37,400 --> 00:24:41,000
Speaker 2: tf IQ weekly Mondays at noon. But you can catch

473
00:24:41,080 --> 00:24:43,080
Speaker 2: me on the clothes every day three to five pm. Anyway,

474
00:24:43,160 --> 00:24:44,080
Speaker 2: go on good Plug.

475
00:24:44,440 --> 00:24:47,920
Speaker 1: And you asked her about the ETF I p O

476
00:24:48,080 --> 00:24:49,679
Speaker 1: heartbeat tray. We talked about a couple of weeks ago.

477
00:24:49,720 --> 00:24:51,159
Speaker 2: There was no way I could not ask her.

478
00:24:52,080 --> 00:24:55,560
Speaker 1: Yeah, so this is the trade where like someone pumps

479
00:24:55,600 --> 00:24:58,760
Speaker 1: like a billion dollars into into an r ETF and

480
00:24:59,600 --> 00:25:03,760
Speaker 1: they do right before some hot IPO and they're betting

481
00:25:03,880 --> 00:25:05,639
Speaker 1: that the ETF is going to get an allocation in

482
00:25:05,680 --> 00:25:07,960
Speaker 1: the IPO and the IPO is going to pop and

483
00:25:08,040 --> 00:25:10,600
Speaker 1: then they will heartbeat out of the ATF and so

484
00:25:10,720 --> 00:25:13,439
Speaker 1: they will have like borrowed shares of all the underlying companies,

485
00:25:13,600 --> 00:25:16,560
Speaker 1: put it into the ETF, taking it right back out again.

486
00:25:16,960 --> 00:25:20,560
Speaker 1: And they've done nothing, like there's no trade there, Like

487
00:25:20,600 --> 00:25:24,399
Speaker 1: they've reversed everything except that they've extracted their portion of

488
00:25:24,440 --> 00:25:28,639
Speaker 1: the IPO pop and monetized that. So if like the

489
00:25:29,160 --> 00:25:31,720
Speaker 1: ETF gets a twenty million dollar IPO allocation and it

490
00:25:31,800 --> 00:25:34,520
Speaker 1: goes up fifty percent, then like there's ten million dollars

491
00:25:34,560 --> 00:25:36,479
Speaker 1: of profits. And if you own half of the ETF

492
00:25:36,600 --> 00:25:38,360
Speaker 1: for like a day, then you get like five million

493
00:25:38,359 --> 00:25:39,560
Speaker 1: dollars of profits with no risk.

494
00:25:39,800 --> 00:25:40,000
Speaker 2: Yeah.

495
00:25:40,240 --> 00:25:43,360
Speaker 1: And so it seems like someone did that a couple

496
00:25:43,400 --> 00:25:45,480
Speaker 1: of times. Yeah, And you asked, Kathy, what about it?

497
00:25:46,040 --> 00:25:48,920
Speaker 2: I did so, first of all, ask for who, she said,

498
00:25:49,200 --> 00:25:52,879
Speaker 2: as I doesn't know, probably a market maker. And she

499
00:25:53,040 --> 00:25:56,480
Speaker 2: pointed out that this takes a lot of guesswork on

500
00:25:56,600 --> 00:25:59,320
Speaker 2: the part of whoever is behind it, the theoretical market

501
00:25:59,359 --> 00:25:59,720
Speaker 2: maker in this.

502
00:26:00,040 --> 00:26:01,800
Speaker 1: Yeah, people got it wrong, like there's like one of

503
00:26:01,840 --> 00:26:04,080
Speaker 1: the big ETFs had one of these big heart beats

504
00:26:04,240 --> 00:26:06,439
Speaker 1: for a big IPA that they didn't get an allocation.

505
00:26:06,760 --> 00:26:09,159
Speaker 2: Yeah, this is according to the FT reporting that it

506
00:26:09,280 --> 00:26:12,880
Speaker 2: was the Arc Innovation ETF. That's their biggest ETF. Basically

507
00:26:13,000 --> 00:26:16,800
Speaker 2: before the Klarna IPO there was that huge heartbeat surge,

508
00:26:17,359 --> 00:26:20,200
Speaker 2: but ARC didn't actually get an allocation in that fund

509
00:26:20,480 --> 00:26:23,840
Speaker 2: to Klarna, so it was kind of a worthless experience,

510
00:26:24,119 --> 00:26:27,560
Speaker 2: whereas they had someone successfully did it with Circle I believe,

511
00:26:27,680 --> 00:26:30,399
Speaker 2: and ARC was a backer of Circle. I think like

512
00:26:30,760 --> 00:26:33,919
Speaker 2: the FT had mentioned that ARC's website hadn't mentioned Klarna

513
00:26:34,040 --> 00:26:37,000
Speaker 2: in some capacity, so you know, maybe it wasn't a

514
00:26:37,119 --> 00:26:41,240
Speaker 2: terrible guess. But I also asked her how she felt

515
00:26:41,280 --> 00:26:43,520
Speaker 2: about it. Actually, my coinker Scarlet Food, askt her how

516
00:26:43,560 --> 00:26:46,320
Speaker 2: she felt about it, Like, was she okay with the

517
00:26:46,440 --> 00:26:49,640
Speaker 2: fact that her funds are just being used as vehicles

518
00:26:50,119 --> 00:26:54,800
Speaker 2: to facilitate these trades, And her answer was she's not upset.

519
00:26:55,400 --> 00:26:58,520
Speaker 2: This is what makes a market and if they guess right, great,

520
00:26:58,680 --> 00:27:01,680
Speaker 2: If they don't, well then they have nothing. They've they've

521
00:27:01,720 --> 00:27:02,719
Speaker 2: incurred some costs.

522
00:27:03,320 --> 00:27:05,640
Speaker 1: I'm just interested in that answer because when we tried

523
00:27:05,640 --> 00:27:07,040
Speaker 1: it with this, like that's kind of what I said.

524
00:27:07,080 --> 00:27:09,080
Speaker 1: I was like ETFs like, like, on the one hand,

525
00:27:09,119 --> 00:27:11,440
Speaker 1: her ETFs are like her investment vehicles. She runs it,

526
00:27:11,520 --> 00:27:14,760
Speaker 1: she's making decisions like you know, it's her fund. But

527
00:27:14,880 --> 00:27:17,080
Speaker 1: on the other hand, ETFs are kind of a piece

528
00:27:17,119 --> 00:27:19,359
Speaker 1: of market plumbing, and like that means they can be

529
00:27:19,480 --> 00:27:23,760
Speaker 1: used by other people mechanically that being her decision. And

530
00:27:23,880 --> 00:27:25,719
Speaker 1: so I think that it's like an interesting like her

531
00:27:25,800 --> 00:27:27,720
Speaker 1: take on this is like, yeah, we're kind of market plumbing,

532
00:27:27,760 --> 00:27:29,399
Speaker 1: and so if that's what people do, that's what people do.

533
00:27:30,119 --> 00:27:33,760
Speaker 1: That's not everyone's take. I heard from one person in

534
00:27:33,840 --> 00:27:36,960
Speaker 1: the ETF world that like won First of all, ETFs

535
00:27:37,040 --> 00:27:39,040
Speaker 1: can turn down creation and redemption trades.

536
00:27:39,160 --> 00:27:42,199
Speaker 2: She said no to that, Okay, depends on.

537
00:27:42,280 --> 00:27:45,720
Speaker 1: Like the authorized participant agreements. But their sense is like

538
00:27:45,800 --> 00:27:48,040
Speaker 1: the norm is that you can turn down. And then

539
00:27:48,080 --> 00:27:51,480
Speaker 1: two like this is rude, yeah, and not nice, and

540
00:27:52,960 --> 00:27:54,720
Speaker 1: because you know, like on the one hand, these are

541
00:27:54,840 --> 00:27:57,639
Speaker 1: ETFs are market plumbing that anyone can kind of trade against,

542
00:27:57,640 --> 00:28:00,720
Speaker 1: but on the other hand, like they are a like

543
00:28:00,880 --> 00:28:04,320
Speaker 1: retail investment product, and if you are an authorised participant,

544
00:28:04,560 --> 00:28:07,680
Speaker 1: if you're a market maker training against these ETFs, like,

545
00:28:08,520 --> 00:28:10,320
Speaker 1: it's kind of your job to be like a nice

546
00:28:10,359 --> 00:28:13,200
Speaker 1: participant in the ecosystem and make them a friendly investment

547
00:28:13,240 --> 00:28:15,919
Speaker 1: product for people. And if you're extracting the IPO premium

548
00:28:15,960 --> 00:28:17,959
Speaker 1: from the ETF, like, that's not nice to the end

549
00:28:18,040 --> 00:28:20,879
Speaker 1: investors and it's like not a constructive thing to do

550
00:28:20,960 --> 00:28:21,760
Speaker 1: in the ecosystem.

551
00:28:21,960 --> 00:28:25,760
Speaker 2: Yeah, I mean, I was also a little bit surprised

552
00:28:25,760 --> 00:28:28,800
Speaker 2: and interested in her answer. It's very emotionally mature, because

553
00:28:29,560 --> 00:28:32,320
Speaker 2: if I put myself into her shoes and Okay, my

554
00:28:32,480 --> 00:28:35,040
Speaker 2: fund is a piece of market plumbing, I accept that.

555
00:28:35,280 --> 00:28:38,800
Speaker 2: But if I have a sink and someone turns on

556
00:28:38,920 --> 00:28:42,320
Speaker 2: the faucet, I would hope they're washing their hands, washing

557
00:28:42,360 --> 00:28:44,960
Speaker 2: their hands in this scenario, always investing in the fund

558
00:28:45,080 --> 00:28:48,520
Speaker 2: because they believe in, you know, my stock picking prowess,

559
00:28:48,600 --> 00:28:50,640
Speaker 2: not that they're just turning on the faucet and then

560
00:28:51,680 --> 00:28:52,520
Speaker 2: doing something else.

561
00:28:52,600 --> 00:28:54,640
Speaker 1: I guess my emotional reaction would be if I woke

562
00:28:54,720 --> 00:28:56,920
Speaker 1: up one day and someone would invested an extra billion

563
00:28:56,960 --> 00:28:58,959
Speaker 1: dollars in my fund, I'd be like sweet. And then

564
00:28:59,040 --> 00:29:01,960
Speaker 1: three days later if they were like they took it

565
00:29:02,000 --> 00:29:04,240
Speaker 1: out again, and be like oh yeah, it would be

566
00:29:04,320 --> 00:29:05,720
Speaker 1: a real emotional roller coaster.

567
00:29:05,720 --> 00:29:09,720
Speaker 2: Like you made my chart so ugly. Yeah, but Kathy,

568
00:29:09,760 --> 00:29:13,160
Speaker 2: would you know, she's not like us, She is built different.

569
00:29:13,400 --> 00:29:16,400
Speaker 1: Yeah. One more thing we have to talk about is that.

570
00:29:16,720 --> 00:29:18,760
Speaker 1: So we had Ryan Patch and John Seal on the

571
00:29:18,800 --> 00:29:23,280
Speaker 1: podcast in May to talk about puzzle hunts generally and

572
00:29:23,560 --> 00:29:26,880
Speaker 1: the Midnight Madness Wall Street puzzle hunt that they run.

573
00:29:26,960 --> 00:29:29,520
Speaker 1: In particular. Midnight Madness was this past weekend.

574
00:29:29,920 --> 00:29:30,840
Speaker 2: Did you participate?

575
00:29:31,520 --> 00:29:35,440
Speaker 1: I did not because for two good for three reasons.

576
00:29:35,800 --> 00:29:37,600
Speaker 2: One is that you were taking care of the possum.

577
00:29:39,080 --> 00:29:41,840
Speaker 1: When is that to be like an entry in Middle

578
00:29:41,920 --> 00:29:44,160
Speaker 1: Badness costs I believe forty two thousand dollars for a

579
00:29:44,200 --> 00:29:46,080
Speaker 1: team of six people. You've got that, which a little

580
00:29:46,080 --> 00:29:48,800
Speaker 1: steep to the team that I have done puzzle hunts

581
00:29:48,800 --> 00:29:50,880
Speaker 1: with before, Like people had other things and so like

582
00:29:51,120 --> 00:29:54,920
Speaker 1: we didn't get the team together. And three, I am

583
00:29:54,960 --> 00:29:57,680
Speaker 1: an old man and it runs from like noon to

584
00:29:57,800 --> 00:30:02,400
Speaker 1: like four am. And the idea of shopping to apparently

585
00:30:02,440 --> 00:30:05,480
Speaker 1: started in Coney Island, and like you know, it goes

586
00:30:05,520 --> 00:30:08,280
Speaker 1: all over the city, and it just it just seemed

587
00:30:08,320 --> 00:30:11,440
Speaker 1: like a little too much for me. It's a young

588
00:30:11,480 --> 00:30:15,560
Speaker 1: man's game. Yeah, it's not only a man's there's a

589
00:30:15,640 --> 00:30:16,240
Speaker 1: gender divide.

590
00:30:16,960 --> 00:30:19,200
Speaker 2: I don't know if that phrase works as well. It's

591
00:30:19,200 --> 00:30:20,240
Speaker 2: a young person's game.

592
00:30:20,360 --> 00:30:23,600
Speaker 1: Yeah, But anyway, it was this weekend. Nineteen teams played.

593
00:30:23,640 --> 00:30:25,920
Speaker 1: There raised eight hundred and eighteen thousand dollars for charity

594
00:30:25,960 --> 00:30:28,520
Speaker 1: for the Good Shepherd Services, and I do want to

595
00:30:28,520 --> 00:30:31,080
Speaker 1: shout out the winners. The winner winner. The first place

596
00:30:31,120 --> 00:30:35,520
Speaker 1: team was Reagan Voke three thousand, which was like the pressure.

597
00:30:35,560 --> 00:30:37,960
Speaker 1: He says, it's a privately funded team. It's like a

598
00:30:38,000 --> 00:30:39,959
Speaker 1: little interesting thing here, which is that you know, it's

599
00:30:40,040 --> 00:30:42,000
Speaker 1: forty two thousand dollars for a team. A lot of

600
00:30:42,080 --> 00:30:45,080
Speaker 1: like financial firms sponsor one or more teams from the firm.

601
00:30:45,920 --> 00:30:50,480
Speaker 1: A lot of other financial firms have their well played

602
00:30:50,480 --> 00:30:55,640
Speaker 1: employees just privately sponsored themselves. And so Reagan Voked three thousand.

603
00:30:55,680 --> 00:30:58,080
Speaker 1: The winning team was privately funded, So I wasn't officially

604
00:30:58,120 --> 00:31:00,800
Speaker 1: sponsored by a firm, but I've heard it was some

605
00:31:00,960 --> 00:31:02,000
Speaker 1: number of Jan's treat people.

606
00:31:02,160 --> 00:31:02,600
Speaker 2: Interesting.

607
00:31:03,160 --> 00:31:05,840
Speaker 1: Second place team was sponsored by Citadel Securities Wow, and

608
00:31:06,040 --> 00:31:08,680
Speaker 1: was called Citadel Insecurities, which is a great team names.

609
00:31:08,920 --> 00:31:10,880
Speaker 1: And then the third place team, Midnight Marauders. I know

610
00:31:10,880 --> 00:31:15,400
Speaker 1: they're privately funded team. Anyway, congratulations on all your success.

611
00:31:15,920 --> 00:31:21,400
Speaker 1: You did it, and that was the Money Stuff Podcast.

612
00:31:21,720 --> 00:31:23,800
Speaker 2: I'm Matt Levine and I'm Katie Greifeld.

613
00:31:24,080 --> 00:31:26,200
Speaker 1: You can find my work by subscribing to The Money

614
00:31:26,240 --> 00:31:28,360
Speaker 1: Stuff newsletter on Bloomberg dot com.

615
00:31:28,600 --> 00:31:31,040
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616
00:31:31,120 --> 00:31:34,000
Speaker 2: on the close between three and five pm Eastern.

617
00:31:34,720 --> 00:31:36,760
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618
00:31:36,760 --> 00:31:40,080
Speaker 1: email to Moneypod at Bloomberg dot net, ask us a

619
00:31:40,200 --> 00:31:42,000
Speaker 1: question and we might answer it on the air.

620
00:31:42,360 --> 00:31:44,960
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621
00:31:45,040 --> 00:31:46,880
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622
00:31:46,920 --> 00:31:47,720
Speaker 2: people find the show.

623
00:31:48,680 --> 00:31:51,560
Speaker 1: The Money Stuff Podcast is produced by Anna ma Aserakis

624
00:31:51,640 --> 00:31:52,920
Speaker 1: and Moses ondam Our.

625
00:31:53,000 --> 00:31:55,000
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626
00:31:55,760 --> 00:31:58,520
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627
00:31:58,640 --> 00:32:00,880
Speaker 2: Stage Bauman is Bloomberg's head of Podcasts.

628
00:32:01,200 --> 00:32:03,640
Speaker 1: Thanks for listening to The Money Stuff Podcast. We'll be

629
00:32:03,720 --> 00:32:05,320
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